The DOI matches its Crossref record; 100 of the 100 references it cites were found in a registry or an index.
Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles
Open accessAbstract
Academic research in the field of autonomous vehicles has reached high popularity in recent years related to several topics as sensor technologies, V2X communications, safety, security, decision making, control, and even legal and standardization rules. Besides classic control design approaches, Artificial Intelligence and Machine Learning methods are present in almost all of these fields. Another part of research focuses on different layers of Motion Planning, such as strategic decisions, trajectory planning, and control. A wide range of techniques in Machine Learning itself have been developed, and this article describes one of these fields, Deep Reinforcement Learning (DRL). The paper provides insight into the hierarchical motion planning problem and describes the basics of DRL. The main elements of designing such a system are the modeling of the environment, the modeling abstractions, the description of the state and the perception models, the appropriate rewarding, and the realization of the underlying neural network. The paper describes vehicle models, simulation possibilities and computational requirements. Strategic decisions on different layers and the observation models, e.g., continuous and discrete state representations, grid-based, and camera-based solutions are presented. The paper surveys the state-of-art solutions systematized by the different tasks and levels of autonomous driving, such as car-following, lane-keeping, trajectory following, merging, or driving in dense traffic. Finally, open questions and future challenges are discussed.
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Cites
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Cited by
Reinforcement learning algorithms: A brief survey
Shakya, A. K., Pillai, G., & Chakrabarty, S. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120495 · Cited by 656
Autonomous driving system: A comprehensive survey
Zhao, J., Zhao, W., Deng, B., Wang, Z., Zhang, F., Zheng, W., Cao, W., Nan, J., Lian, Y., & Burke, A. F. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122836 · Cited by 361
Machine Learning for Security in Vehicular Networks: A Comprehensive Survey
Talpur, A., & Gurusamy, M. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3129079 · Cited by 107
Target tracking control of UAV through deep reinforcement learning
Ma, B., Liu, Z., Zhao, W., Yuan, J., Long, H., Wang, X., & Yuan, Z. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3249900 · Cited by 91
Huang, Z., Sheng, Z., Ma, C., & Chen, S. (2024) · Communications in Transportation Research · doi:10.1016/j.commtr.2024.100127 · Cited by 88
Metadata
- Authors
- Aradi, S.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2022-02
- Volume
- 23
- Issue
- 2
- Pages
- 740-759
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W3004270765
- Cited by
- 639
- References
- 100
- FWCI
- 22.49
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
